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186 tahun, sedangkan data pelatihan yang digunakan 2004-2009 tidak menunjukkan indikasi La Nina. B. Saran 1. Penelitian kedepan dapat dikembangkan dengan beberapa model hybrid kecerdasan buatan dan statistik yang belum pernah digunakan. Potensi hybrid 3 model dapat saja dilakukan untuk prediksi yang lebih baik dengan konsekuensi kompleksitas yang lebih tinggi. 2. Penggunaan sampling resolusi harian yang selama ini digunakan dalam penelitian-penelitian sebelumnya sebaiknya ditinjau ulang. Saran kepada pihak BMKG dan lembaga terkait untuk juga dapat menyediakan data sampling dengan resolusi per jam. Dengan adanya resolusi sampling yang lebih cepat akan memberikan gambaran korelasi unsur meteorologi yang lebih akurat dan berujung ke hasil prediksi yang lebih baik. DAFTAR PUSTAKA Abhishek, k., Kumar A, 2012, A Rainfall Prediction Model using Artificial Neural Network, IEEE Control and System Graduate Research Colloquium. PDF Create! 4 Trial www.nuance.com

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186

tahun, sedangkan data pelatihan yang digunakan 2004-2009 tidak

menunjukkan indikasi La Nina.

B. Saran

1. Penelitian kedepan dapat dikembangkan dengan beberapa model hybrid

kecerdasan buatan dan statistik yang belum pernah digunakan. Potensi

hybrid 3 model dapat saja dilakukan untuk prediksi yang lebih baik dengan

konsekuensi kompleksitas yang lebih tinggi.

2. Penggunaan sampling resolusi harian yang selama ini digunakan dalam

penelitian-penelitian sebelumnya sebaiknya ditinjau ulang. Saran kepada

pihak BMKG dan lembaga terkait untuk juga dapat menyediakan data

sampling dengan resolusi per jam. Dengan adanya resolusi sampling yang

lebih cepat akan memberikan gambaran korelasi unsur meteorologi yang

lebih akurat dan berujung ke hasil prediksi yang lebih baik.

DAFTAR PUSTAKA

Abhishek, k., Kumar A, 2012, A Rainfall Prediction Model using Artificial Neural

Network, IEEE Control and System Graduate Research Colloquium.

PDF Crea

te! 4

Trial

www.nuan

ce.co

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Page 2: B. Saran PDF Create! 4 Trial

187

Aldrian, E., Susanto, R.D. 2003. Identification of Three Dominant Rainfall

Regions Within Indonesia and Their Relationship to Sea Surface

Temperature. Int. J. Climatol. 23: 1435–1452.

Aldrian, E., Djamil, YS. 2008. Application of Multivariate Anfis for Daily Rainfall

Prediction: Influences of Training Data Size. MAKARA, SAINS. Volume12, No. 1.

Chen, Y., Luh P.B, Guan, C, Zhao, Y, Michel, L.D, Coolbeth, M, 2010. Short-

Term Load Forecasting: Similar Day-Based Wavelet Neural Networks.IEEE TRANSACTIONS ON POWER SYSTEMS. VOL. 25, NO. 1.

Demuth, 2009. Advanced in Neural Network, 6th International Symposium on

Neural Networks, ISNN 2009 Wuhan, China.

French, M. N., Krajewski, W. F., and Cuykendall, R. R,1992.: Rainfall

forecasting in space and time using neural network, J. Hydrol.,137, 1–31.

Gan, X., 2011. The research of rainfall prediction models based on Matlab

neural network. Cloud Computing and Intelligence Systems (CCIS),2011 IEEE.

Giannini, A., Robertson, W.A, Qian, J-H, 2007. A role for tropical tropospheric

temperature adjustment to El Nin˜o–Southern Oscillation in the

seasonality of monsoonal Indonesia precipitation predictability. Journal

of Geophysical Research. VOL. 112, D16110.

Han, Dawei, 2010. Concise Hydrology, Denmark : Ventus Publishing Aps.

PDF Crea

te! 4

Trial

www.nuan

ce.co

m

Page 3: B. Saran PDF Create! 4 Trial

188

Hongxia, L., Chuanwei L, 2008. Construction and Application of Fuzzy Neural

Network Model in Precipitation Forecast of Sanjiang Plain, China.

International Conference on Wireless Communication, Networking andMobile Computing (WiCOM).

Indrabayu, 2011. Neural Network and Fuzzy methods for rainfall prediction,Proc. The 1stFortei Conference, Makassar, Indonesia, 2011,pp135 (InIndonesian)

Iskandar, 2010, Seasonal and interannual pattern of sea surface temperature

in Banda Sea as revealed by self-organizing maps, Continental ShelfRes. May 31, 2010

Istriana, 2009. Pemodelan Curah Hujan Dengan Pendekatan Adaptive Spline

Treshold Autoregressive (ASTAR). Fromhttp://digilib.its.ac.id/pemodelan-curah-hujan-dengan-pendekatanadaptive-spline-threshold-autoregressiveastar-6441.html.22 Desember 2009.

Kumar, A., Yang, F, Goddard L, Schubert, S. 2004. Differing Trends in the

Tropical Surface Temperatures and Precipitation over Land and

Oceans. Journal of Climate. Vol.17.

Knap, Brian.J, 1979. Element Of Geographical Hydrology, London : AcademicDivision Of Unwin Hyman LTD.

Lin, K., Lin, J, Chen, B, 2004. Study on Short-range Precipitation Forecasting

Method Based on Genetic Algorithm Neural Network, 7th WorldCongress on Intelligent Control and Automation, Chongqing, China.IEEE Explore.

Luo, F., Wu, C, Wu J, 2010. A Novel Neural Network Ensemble Model Based

on Sample Reconstruction and Projection Pursuit for Rainfall

PDF Crea

te! 4

Trial

www.nuan

ce.co

m

Page 4: B. Saran PDF Create! 4 Trial

189

Forecasting. Sixth International Conference on Natural Computation(ICNC). IEEE Explore.

Lundquist, Jessica, 2010. “Hidrologic Process”. Fromhttp://faculty.washington.edu/jdlund/classes/CEE345/Lundquist_ebook

_2010_Hydrology.pdf

Mc Cuen,Richard.H,1998. Hydrologic Analysis And Design Second Edition,

United States : Pretince Hall PTR.

Medvigy, D., and C. Beaulieu, 2012. Changes in daily solar radiation and

precipitation coefficients of variation since 1984. J. Climate, 25, 1330-1339.

Manusthiparom C., Oki T., and Kanae, S. 2003. Quantitative Rainfall

Prediction in Thailand, First International Conference on Hydrology andWater Resources on Asia Pacific Region (APHW),Kyoto, Japan.

M.C. Ramirez, H.F. Velho, 2005. Artificial neural network technique for rainfall

forecasting applied to the São Paulo region, Journal of HydrologyVolume 301, Issues 1-4.

Mendelsohn, Lou; Stein, Jon, 2007, Fundamental Analysis Meets the Neural

Network. Magazine article from Futures (Cedar Falls, IA), Vol. 20, No.10.

N. Q. Hung, M. S. Babel, S. Weesakul, and N. K. Tripathi, 2009. An artificial

neural network model for rainfall forecasting in Bangkok, Thailand.Hydrol. Earth Syst. Sci., 13, 1413–1425.

Nong, J., Jin, L, 2008. Application of Support Vector Machine to Predict

Precipitation. Proceedings of the 7th World Congress on IntelligentControl and Automation. Chongqing, China. IEEE Explore.

PDF Crea

te! 4

Trial

www.nuan

ce.co

m

Page 5: B. Saran PDF Create! 4 Trial

190

Nong, J. 2010. The Short-range Precipitation Forecasting Method of Neural

Network Based on Principal Component Analysis. Third InternationalJoint Conference on Computational Science and Optimization. IEEEExplore.

Ragunath,H.M,2007.Hydrology : Principle, Analysis, Design, New Delhi : NewAge International Publisher.

Sheng, LL., Cheng, WQ, Xia, H, Zhang X, 2010. Prediction Of Annual

Precipitation Based On Fuzzy And Grey Markov Process. Proceedingsof the Ninth International Conference on Machine Learning andCybernetics, Qingdao.

Solaimani, K, 2009. Rainfall-runoff Prediction Based on Artificial Neural

Network (A Case Study: Jarahi Watershed). American-Eurasian

Journal. Agric. & Environ. Sci. 5 (6): 856-865. IDOSI Publications.

Sonjaya, I., Kurniawan T, 2009. Uji Aplikasi Hybmg Versi 2.0 untuk Prakiraan

Curah Hujan Pola Monsunal Ekuatorial dan Lokal. Buletin Meteorologi

Klimatologi Dan Geofisika. Vol.5 No.3.

Speer, K., N. Lovenduski, M. H. England, D. W. J. Thompson, C. Beswick,2012: Developing a vision for climate variability research in the

Southern Ocean-Ice-Atmosphere system, CLIVAR Exchanges, 17, No.1, 43-45

Susilowati, 2010. Pokok-pokok Klimatologi, Ganesha Bandung.

PDF Crea

te! 4

Trial

www.nuan

ce.co

m

Page 6: B. Saran PDF Create! 4 Trial

191

Subarna, D. 2009. Aplikasi Jaringan Neural Untuk Pemodelan dan Prediksi

Curah Hujan. Berita Dirgantara. Vol.10 No.1.

Shaw, Elizabeth. M., Beven, Keith, J, Chapbell, Nick, A, Lamb, Rob. 2011.Hydrology In Practice Fourth Edition. Oxon : Spon Press.

T. Terasvirta, A.B. Kock, 2010, Forecasting with Nonlinear Time Series

Models, CREATES Research Paper No. 2010-1.

Warsito, B., Sumiyati, S, 2007. Prediksi Curah Hujan Kota Semarang Dengan

Feedforward Neural Network Menggunakan Algoritma Quasi Newton

Bfgs Dan Levenberg-Marquardt. Jurnal PRESIPITASI. Vol. 3 No.2.

Wu H., Lin X, 2009. Application of Fuzzy Neural Network to the Flood Season

Precipitation Forecast. International Joint Conference onComputational Sciences and Optimization. IEEE Explore.

Zao J., Astron J, 2004, The Effect of Solar Activity on the Annual Precipitation

in the Beijing Area, Chinese Journal of Astronomy and AstrophysicsVolume 4 Number 2.

PDF Crea

te! 4

Trial

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